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    Strengthening electron-triggered light emission

    The way electrons interact with photons of light is a key part of many modern technologies, from lasers to solar panels to LEDs. But the interaction is inherently a weak one because of a major mismatch in scale: A wavelength of visible light is about 1,000 times larger than an electron, so the way the two things affect each other is limited by that disparity.

    Now, researchers at MIT and elsewhere have come up with an innovative way to make much stronger interactions between photons and electrons possible, in the process producing a hundredfold increase in the emission of light from a phenomenon called Smith-Purcell radiation. The finding has potential implications for both commercial applications and fundamental scientific research, although it will require more years of research to make it practical.

    The findings are reported today in the journal Nature, in a paper by MIT postdocs Yi Yang (now an assistant professor at the University of Hong Kong) and Charles Roques-Carmes, MIT professors Marin Soljačić and John Joannopoulos, and five others at MIT, Harvard University, and Technion-Israel Institute of Technology.

    In a combination of computer simulations and laboratory experiments, the team found that using a beam of electrons in combination with a specially designed photonic crystal — a slab of silicon on an insulator, etched with an array of nanometer-scale holes — they could theoretically predict stronger emission by many orders of magnitude than would ordinarily be possible in conventional Smith-Purcell radiation. They also experimentally recorded a one hundredfold increase in radiation in their proof-of-concept measurements.

    Unlike other approaches to producing sources of light or other electromagnetic radiation, the free-electron-based method is fully tunable — it can produce emissions of any desired wavelength, simply by adjusting the size of the photonic structure and the speed of the electrons. This may make it especially valuable for making sources of emission at wavelengths that are difficult to produce efficiently, including terahertz waves, ultraviolet light, and X-rays.

    The team has so far demonstrated the hundredfold enhancement in emission using a repurposed electron microscope to function as an electron beam source. But they say that the basic principle involved could potentially enable far greater enhancements using devices specifically adapted for this function.

    The approach is based on a concept called flatbands, which have been widely explored in recent years for condensed matter physics and photonics but have never been applied to affecting the basic interaction of photons and free electrons. The underlying principle involves the transfer of momentum from the electron to a group of photons, or vice versa. Whereas conventional light-electron interactions rely on producing light at a single angle, the photonic crystal is tuned in such a way that it enables the production of a whole range of angles.

    The same process could also be used in the opposite direction, using resonant light waves to propel electrons, increasing their velocity in a way that could potentially be harnessed to build miniaturized particle accelerators on a chip. These might ultimately be able to perform some functions that currently require giant underground tunnels, such as the 30-kilometer-wide Large Hadron Collider in Switzerland.

    “If you could actually build electron accelerators on a chip,” Soljačić says, “you could make much more compact accelerators for some of the applications of interest, which would still produce very energetic electrons. That obviously would be huge. For many applications, you wouldn’t have to build these huge facilities.”

    The new system could also potentially provide a highly controllable X-ray beam for radiotherapy purposes, Roques-Carmes says.

    And the system could be used to generate multiple entangled photons, a quantum effect that could be useful in the creation of quantum-based computational and communications systems, the researchers say. “You can use electrons to couple many photons together, which is a considerably hard problem if using a purely optical approach,” says Yang. “That is one of the most exciting future directions of our work.”

    Much work remains to translate these new findings into practical devices, Soljačić cautions. It may take some years to develop the necessary interfaces between the optical and electronic components and how to connect them on a single chip, and to develop the necessary on-chip electron source producing a continuous wavefront, among other challenges.

    “The reason this is exciting,” Roques-Carmes adds, “is because this is quite a different type of source.” While most technologies for generating light are restricted to very specific ranges of color or wavelength, and “it’s usually difficult to move that emission frequency. Here it’s completely tunable. Simply by changing the velocity of the electrons, you can change the emission frequency. … That excites us about the potential of these sources. Because they’re different, they offer new types of opportunities.”

    But, Soljačić concludes, “in order for them to become truly competitive with other types of sources, I think it will require some more years of research. I would say that with some serious effort, in two to five years they might start competing in at least some areas of radiation.”

    The research team also included Steven Kooi at MIT’s Institute for Soldier Nanotechnologies, Haoning Tang and Eric Mazur at Harvard University, Justin Beroz at MIT, and Ido Kaminer at Technion-Israel Institute of Technology. The work was supported by the U.S. Army Research Office through the Institute for Soldier Nanotechnologies, the U.S. Air Force Office of Scientific Research, and the U.S. Office of Naval Research. More

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    Pursuing a practical approach to research

    Koroush Shirvan, the John Clark Hardwick Career Development Professor in the Department of Nuclear Science and Engineering (NSE), knows that the nuclear industry has traditionally been wary of innovations until they are shown to have proven utility. As a result, he has relentlessly focused on practical applications in his research, work that has netted him the 2022 Reactor Technology Award from the American Nuclear Society. “The award has usually recognized practical contributions to the field of reactor design and has not often gone to academia,” Shirvan says.

    One of these “practical contributions” is in the field of accident-tolerant fuels, a program launched by the U.S. Nuclear Regulatory Commission in the wake of the 2011 Fukushima Daiichi incident. The goal within this program, says Shirvan, is to develop new forms of nuclear fuels that can tolerate heat. His team, with students from over 16 countries, is working on numerous possibilities that range in composition and method of production.

    Another aspect of Shirvan’s research focuses on how radiation impacts heat transfer mechanisms in the reactor. The team found fuel corrosion to be the driving force. “[The research] informs how nuclear fuels perform in the reactor, from a practical point of view,” Shirvan says.

    Optimizing nuclear reactor design

    A summer internship when Shirvan was an undergraduate at the University of Florida at Gainesville seeded his drive to focus on practical applications in his studies. A nearby nuclear utility was losing millions because of crud accumulating on fuel rods. Over time, the company was solving the problem by using more fuel, before it had extracted all the life from earlier batches.

    Placement of fuel rods in nuclear reactors is a complex problem with many factors — the life of the fuel, location of hot spots — affecting outcomes. Nuclear reactors change their configuration of fuel rods every 18-24 months to optimize close to 15-20 constraints, leading to roughly 200-800 assemblies. The mind-boggling nature of the problem means that plants have to rely on experienced engineers.

    During his internship, Shirvan optimized the program used to place fuel rods in the reactor. He found that certain rods in assemblies were more prone to the crud deposits, and reworked their configurations, optimizing for these rods’ performance instead of adding assemblies.

    In recent years, Shirvan has applied a branch of artificial intelligence — reinforcement learning — to the configuration problem and created a software program used by the largest U.S. nuclear utility. “This program gives even a layperson the ability to reconfigure the fuels and the reactor without having expert knowledge,” Shirvan says.

    From advanced math to counting jelly beans

    Shirvan’s own expertise in nuclear science and engineering developed quite organically. He grew up in Tehran, Iran, and when he was 14 the family moved to Gainesville, where Shirvan’s aunt and family live. He remembers an awkward couple of years at the new high school where he was grouped in with newly arrived international students, and placed in entry-level classes. “I went from doing advanced mathematics in Iran to counting jelly beans,” he laughs.

    Shirvan applied to the University of Florida for his undergraduate studies since it made economic sense; the school gave full scholarships to Floridian students who received a certain minimum SAT score. Shirvan qualified. His uncle, who was a professor in the nuclear engineering department then, encouraged Shirvan to take classes in the department. Under his uncle’s mentorship, the courses Shirvan took, and his internship, cemented his love of the interdisciplinary approach that the field demanded.

    Having always known that he wanted to teach — he remembers finishing his math tests early in Tehran so he could earn the reward of being class monitor — Shirvan knew graduate school was next. His uncle encouraged him to apply to MIT and to the University of Michigan, home to reputable programs in the field. Shirvan chose MIT because “only at MIT was there a program on nuclear design. There were faculty dedicated to designing new reactors, looking at multiple disciplines, and putting all of that together.” He went on to pursue his master’s and doctoral studies at NSE under the supervision of Professor Mujid Kazimi, focusing on compact pressurized and boiling water reactor designs. When Kazimi passed away suddenly in 2015, Shirvan was a research scientist, and switched to tenure track to guide the professor’s team.

    Another project that Shirvan took in 2015: leadership of MIT’s course on nuclear reactor technology for utility executives. Offered only by the Institute, the program is an introduction to nuclear engineering and safety for personnel who might not have much background in the area. “It’s a great course because you get to see what the real problems are in the energy sector … like grid stability,” Shirvan says.

    A multipronged approach to savings

    Another very real problem nuclear utilities face is cost. Contrary to what one hears on the news, one of the biggest stumbling blocks to building new nuclear facilities in the United States is cost, which today can be up to three times that of renewables, Shirvan says. While many approaches such as advanced manufacturing have been tried, Shirvan believes that the solution to decrease expenditures lies in designing more compact reactors.

    His team has developed an open-source advanced nuclear cost tool and has focused on two different designs: a small water reactor using compact steam technology and a horizontal gas reactor. Compactness also means making fuels more efficient, as Shirvan’s work does, and in improving the heat exchange device. It’s all back to the basics and bringing “commercial viable arguments in with your research,” Shirvan explains.

    Shirvan is excited about the future of the U.S. nuclear industry, and that the 2022 Inflation Reduction Act grants the same subsidies to nuclear as it does for renewables. In this new level playing field, advanced nuclear still has a long way to go in terms of affordability, he admits. “It’s time to push forward with cost-effective design,” Shirvan says, “I look forward to supporting this by continuing to guide these efforts with research from my team.” More

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    Machine learning facilitates “turbulence tracking” in fusion reactors

    Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

    A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

    Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

    The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

    When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

    Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

    “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

    His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

    Heating things up

    For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

    A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

    However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

    “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

    Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

    “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

    He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

    Simulating blobs

    To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

    “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

    Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

    Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

    Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

    But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

    The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

    Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

    They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

    “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

    This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

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    Computing for the health of the planet

    The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

    Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

    The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

    The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty — 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

    Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

    “By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.”

    “These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overarching effort to hire climate-focused faculty — introducing the critical role of computing to address the health of the planet through innovative research and curriculum.”

    The four new faculty members are:

    Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

    Priya Donti will join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24. Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

    Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

    Sherrie Wang will join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell. More

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    Taking a magnifying glass to data center operations

    When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner — and the team is looking for ways to improve.

    “We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC. 

    To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

    Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow. They also see potential for leveraging AI in the data center itself, by using the data to develop models for predicting failure points, optimizing job scheduling, and improving energy efficiency. While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage. The release of this dataset and associated code seeks to fill this space.

    “Data centers are changing. We have an explosion of hardware platforms, the types of workloads are evolving, and the types of people who are using data centers is changing,” says Vijay Gadepally, a senior researcher at the LLSC. “Until now, there hasn’t been a great way to analyze the impact to data centers. We see this research and dataset as a big step toward coming up with a principled approach to understanding how these variables interact with each other and then applying AI for insights and improvements.”

    Papers describing the dataset and potential applications have been accepted to a number of venues, including the IEEE International Symposium on High-Performance Computer Architecture, the IEEE International Parallel and Distributed Processing Symposium, the Annual Conference of the North American Chapter of the Association for Computational Linguistics, the IEEE High-Performance and Embedded Computing Conference, and International Conference for High Performance Computing, Networking, Storage and Analysis. 

    Workload classification

    Among the world’s TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accelerators. These NVIDIA GPUs are specialized for deep learning, the class of AI that has given rise to speech recognition and computer vision.

    The dataset covers CPU, GPU, and memory usage by job; scheduling logs; and physical monitoring data. Compared to similar datasets, such as those from Google and Microsoft, the LLSC dataset offers “labeled data, a variety of known AI workloads, and more detailed time series data compared with prior datasets. To our knowledge, it’s one of the most comprehensive and fine-grained datasets available,” Gadepally says. 

    Notably, the team collected time-series data at an unprecedented level of detail: 100-millisecond intervals on every GPU and 10-second intervals on every CPU, as the machines processed more than 3,000 known deep-learning jobs. One of the first goals is to use this labeled dataset to characterize the workloads that different types of deep-learning jobs place on the system. This process would extract features that reveal differences in how the hardware processes natural language models versus image classification or materials design models, for example.   

    The team has now launched the MIT Datacenter Challenge to mobilize this research. The challenge invites researchers to use AI techniques to identify with 95 percent accuracy the type of job that was run, using their labeled time-series data as ground truth.

    Such insights could enable data centers to better match a user’s job request with the hardware best suited for it, potentially conserving energy and improving system performance. Classifying workloads could also allow operators to quickly notice discrepancies resulting from hardware failures, inefficient data access patterns, or unauthorized usage.

    Too many choices

    Today, the LLSC offers tools that let users submit their job and select the processors they want to use, “but it’s a lot of guesswork on the part of users,” Samsi says. “Somebody might want to use the latest GPU, but maybe their computation doesn’t actually need it and they could get just as impressive results on CPUs, or lower-powered machines.”

    Professor Devesh Tiwari at Northeastern University is working with the LLSC team to develop techniques that can help users match their workloads to appropriate hardware. Tiwari explains that the emergence of different types of AI accelerators, GPUs, and CPUs has left users suffering from too many choices. Without the right tools to take advantage of this heterogeneity, they are missing out on the benefits: better performance, lower costs, and greater productivity.

    “We are fixing this very capability gap — making users more productive and helping users do science better and faster without worrying about managing heterogeneous hardware,” says Tiwari. “My PhD student, Baolin Li, is building new capabilities and tools to help HPC users leverage heterogeneity near-optimally without user intervention, using techniques grounded in Bayesian optimization and other learning-based optimization methods. But, this is just the beginning. We are looking into ways to introduce heterogeneity in our data centers in a principled approach to help our users achieve the maximum advantage of heterogeneity autonomously and cost-effectively.”

    Workload classification is the first of many problems to be posed through the Datacenter Challenge. Others include developing AI techniques to predict job failures, conserve energy, or create job scheduling approaches that improve data center cooling efficiencies.

    Energy conservation 

    To mobilize research into greener computing, the team is also planning to release an environmental dataset of TX-GAIA operations, containing rack temperature, power consumption, and other relevant data.

    According to the researchers, huge opportunities exist to improve the power efficiency of HPC systems being used for AI processing. As one example, recent work in the LLSC determined that simple hardware tuning, such as limiting the amount of power an individual GPU can draw, could reduce the energy cost of training an AI model by 20 percent, with only modest increases in computing time. “This reduction translates to approximately an entire week’s worth of household energy for a mere three-hour time increase,” Gadepally says.

    They have also been developing techniques to predict model accuracy, so that users can quickly terminate experiments that are unlikely to yield meaningful results, saving energy. The Datacenter Challenge will share relevant data to enable researchers to explore other opportunities to conserve energy.

    The team expects that lessons learned from this research can be applied to the thousands of data centers operated by the U.S. Department of Defense. The U.S. Air Force is a sponsor of this work, which is being conducted under the USAF-MIT AI Accelerator.

    Other collaborators include researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Charles Leiserson’s Supertech Research Group is investigating performance-enhancing techniques for parallel computing, and research scientist Neil Thompson is designing studies on ways to nudge data center users toward climate-friendly behavior.

    Samsi presented this work at the inaugural AI for Datacenter Optimization (ADOPT’22) workshop last spring as part of the IEEE International Parallel and Distributed Processing Symposium. The workshop officially introduced their Datacenter Challenge to the HPC community.

    “We hope this research will allow us and others who run supercomputing centers to be more responsive to user needs while also reducing the energy consumption at the center level,” Samsi says. More

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    Building better batteries, faster

    To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. One of their main challenges? Figuring out how to make extremely powerful but lightweight batteries.

    Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

    With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries. Such batteries would not only improve the range of EVs, but they could also unlock potential in other high-power systems, such as solar energy systems that continuously deliver power, even at night.

    From a young age, Leon knew he wanted to pursue a PhD, hoping to one day become a professor of engineering, like his father. Growing up in College Station, Texas, home to Texas A&M University, where his father worked, many of Leon’s friends also had parents who were professors or affiliated with the university. Meanwhile, his mom worked outside the university, as a family counselor in a neighboring city.

    In college, Leon followed in his father’s and older brother’s footsteps to become a mechanical engineer, earning his bachelor’s degree at Texas A&M. There, he learned how to model the behaviors of mechanical systems, such as a metal spring’s stiffness. But he wanted to delve deeper, down to the level of atoms, to understand exactly where these behaviors come from.

    So, when Leon applied to graduate school at MIT, he switched fields to materials science, hoping to satisfy his curiosity. But the transition to a different field was “a really hard process,” Leon says, as he rushed to catch up to his peers.

    To help with the transition, Leon sought out a congenial research advisor and found one in Rafael Gómez-Bombarelli, an assistant professor in the Department of Materials Science and Engineering (DMSE). “Because he’s from Spain and my parents are Peruvian, there’s a cultural ease with the way we talk,” Leon says. According to Gómez-Bombarelli, sometimes the two of them even discuss research in Spanish — a “rare treat.” That connection has empowered Leon to freely brainstorm ideas or talk through concerns with his advisor, enabling him to make significant progress in his research.

    Leveraging machine learning to research battery materials

    Scientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform. These simulations act as virtual microscopes for batteries, zooming in to see how materials interact at an atomic level. With these details, scientists can understand why certain combinations do better, guiding their search for high-performing materials.

    But building accurate computer simulations is extremely time-intensive, taking years and sometimes even decades. “You need to know how every atom interacts with every other atom in your system,” Leon says. To create a computer model of these interactions, scientists first make a rough guess at a model using complex quantum mechanics calculations. They then compare the model with results from real-life experiments, manually tweaking different parts of the model, including the distances between atoms and the strength of chemical bonds, until the simulation matches real life.

    With well-studied battery materials, the simulation process is somewhat easier. Scientists can buy simulation software that includes pre-made models, Leon says, but these models often have errors and still require additional tweaking.

    To build accurate computer models more quickly, Leon is developing a machine-learning-based tool that can efficiently guide the trial-and-error process. “The hope with our machine learning framework is to not have to rely on proprietary models or do any hand-tuning,” he says. Leon has verified that for well-studied materials, his tool is as accurate as the manual method for building models.

    With this system, scientists will have a single, standardized approach for building accurate models in lieu of the patchwork of approaches currently in place, Leon says.

    Leon’s tool comes at an opportune time, when many scientists are investigating a new paradigm of batteries: solid-state batteries. Compared to traditional batteries, which contain liquid electrolytes, solid-state batteries are safer, lighter, and easier to manufacture. But creating versions of these batteries that are powerful enough for EVs or renewable energy storage is challenging.

    This is largely because in battery chemistry, ions dislike flowing through solids and instead prefer liquids, in which atoms are spaced further apart. Still, scientists believe that with the right combination of materials, solid-state batteries can provide enough electricity for high-power systems, such as EVs. 

    Leon plans to use his machine-learning tool to help look for good solid-state battery materials more quickly. After he finds some powerful candidates in simulations, he’ll work with other scientists to test out the new materials in real-world experiments.

    Helping students navigate graduate school

    To get to where he is today, doing exciting and impactful research, Leon credits his community of family and mentors. Because of his upbringing, Leon knew early on which steps he would need to take to get into graduate school and work toward becoming a professor. And he appreciates the privilege of his position, even more so as a Peruvian American, given that many Latino students are less likely to have access to the same resources. “I understand the academic pipeline in a way that I think a lot of minority groups in academia don’t,” he says.

    Now, Leon is helping prospective graduate students from underrepresented backgrounds navigate the pipeline through the DMSE Application Assistance Program. Each fall, he mentors applicants for the DMSE PhD program at MIT, providing feedback on their applications and resumes. The assistance program is student-run and separate from the admissions process.

    Knowing firsthand how invaluable mentorship is from his relationship with his advisor, Leon is also heavily involved in mentoring junior PhD students in his department. This past year, he served as the academic chair on his department’s graduate student organization, the Graduate Materials Council. With MIT still experiencing disruptions from Covid-19, Leon noticed a problem with student cohesiveness. “I realized that traditional [informal] modes of communication across [incoming class] years had been cut off,” he says, making it harder for junior students to get advice from their senior peers. “They didn’t have any community to fall back on.”

    To help fix this problem, Leon served as a go-to mentor for many junior students. He helped second-year PhD students prepare for their doctoral qualification exam, an often-stressful rite of passage. He also hosted seminars for first-year students to teach them how to make the most of their classes and help them acclimate to the department’s fast-paced classes. For fun, Leon organized an axe-throwing event to further facilitate student cameraderie.

    Leon’s efforts were met with success. Now, “newer students are building back the community,” he says, “so I feel like I can take a step back” from being academic chair. He will instead continue mentoring junior students through other programs within the department. He also plans to extend his community-building efforts among faculty and students, facilitating opportunities for students to find good mentors and work on impactful research. With these efforts, Leon hopes to help others along the academic pipeline that he’s become familiar with, journeying together over their PhDs. More

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    Solving a longstanding conundrum in heat transfer

    It is a problem that has beguiled scientists for a century. But, buoyed by a $625,000 Distinguished Early Career Award from the U.S. Department of Energy (DoE), Matteo Bucci, an associate professor in the Department of Nuclear Science and Engineering (NSE), hopes to be close to an answer.

    Tackling the boiling crisis

    Whether you’re heating a pot of water for pasta or are designing nuclear reactors, one phenomenon — boiling — is vital for efficient execution of both processes.

    “Boiling is a very effective heat transfer mechanism; it’s the way to remove large amounts of heat from the surface, which is why it is used in many high-power density applications,” Bucci says. An example use case: nuclear reactors.

    To the layperson, boiling appears simple — bubbles form and burst, removing heat. But what if so many bubbles form and coalesce that they form a band of vapor that prevents further heat transfer? Such a problem is a known entity and is labeled the boiling crisis. It would lead to runaway heat, and a failure of fuel rods in nuclear reactors. So “understanding and determining under which conditions the boiling crisis is likely to happen is critical to designing more efficient and cost-competitive nuclear reactors,” Bucci says.

    Early work on the boiling crisis dates back nearly a century ago, to 1926. And while much work has been done, “it is clear that we haven’t found an answer,” Bucci says. The boiling crisis remains a challenge because while models abound, the measurement of related phenomena to prove or disprove these models has been difficult. “[Boiling] is a process that happens on a very, very small length scale and over very, very short times,” Bucci says. “We are not able to observe it at the level of detail necessary to understand what really happens and validate hypotheses.”

    But, over the past few years, Bucci and his team have been developing diagnostics that can measure the phenomena related to boiling and thereby provide much-needed answers to a classic problem. Diagnostics are anchored in infrared thermometry and a technique using visible light. “By combining these two techniques I think we’re going to be ready to answer standing questions related to heat transfer, we can make our way out of the rabbit hole,” Bucci says. The grant award from the U.S. DoE for Nuclear Energy Projects will aid in this and Bucci’s other research efforts.

    An idyllic Italian childhood

    Tackling difficult problems is not new territory for Bucci, who grew up in the small town of Città di Castello near Florence, Italy. Bucci’s mother was an elementary school teacher. His father used to have a machine shop, which helped develop Bucci’s scientific bent. “I liked LEGOs a lot when I was a kid. It was a passion,” he adds.

    Despite Italy going through a severe pullback from nuclear engineering during his formative years, the subject fascinated Bucci. Job opportunities in the field were uncertain but Bucci decided to dig in. “If I have to do something for the rest of my life, it might as well be something I like,” he jokes. Bucci attended the University of Pisa for undergraduate and graduate studies in nuclear engineering.

    His interest in heat transfer mechanisms took root during his doctoral studies, a research subject he pursued in Paris at the French Alternative Energies and Atomic Energy Commission (CEA). It was there that a colleague suggested work on the boiling water crisis. This time Bucci set his sights on NSE at MIT and reached out to Professor Jacopo Buongiorno to inquire about research at the institution. Bucci had to fundraise at CEA to conduct research at MIT. He arrived just a couple of days before the Boston Marathon bombing in 2013 with a round-trip ticket. But Bucci has stayed ever since, moving on to become a research scientist and then associate professor at NSE.

    Bucci admits he struggled to adapt to the environment when he first arrived at MIT, but work and friendships with colleagues — he counts NSE’s Guanyu Su and Reza Azizian as among his best friends — helped conquer early worries.

    The integration of artificial intelligence

    In addition to diagnostics for boiling, Bucci and his team are working on ways of integrating artificial intelligence and experimental research. He is convinced that “the integration of advanced diagnostics, machine learning, and advanced modeling tools will blossom in a decade.”

    Bucci’s team is developing an autonomous laboratory for boiling heat transfer experiments. Running on machine learning, the setup decides which experiments to run based on a learning objective the team assigns. “We formulate a question and the machine will answer by optimizing the kinds of experiments that are necessary to answer those questions,” Bucci says, “I honestly think this is the next frontier for boiling,” he adds.

    “It’s when you climb a tree and you reach the top, that you realize that the horizon is much more vast and also more beautiful,” Bucci says of his zeal to pursue more research in the field.

    Even as he seeks new heights, Bucci has not forgotten his origins. Commemorating Italy’s hosting of the World Cup in 1990, a series of posters showcasing a soccer field fitted into the Roman Colosseum occupies pride of place in his home and office. Created by Alberto Burri, the posters are of sentimental value: The (now deceased) Italian artist also hailed from Bucci’s hometown — Città di Castello. More

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    New hardware offers faster computation for artificial intelligence, with much less energy

    As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

    Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

    A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

    Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

    “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

    “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

    “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

    These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

    “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

    Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

    Accelerating deep learning

    Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

    The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

    In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

    The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

    To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

    PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

    Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

    Surprising speed

    PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

    “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

    “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

    Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

    Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

    Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

    At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

    “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

    “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

    “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

    “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

    This research is funded, in part, by the MIT-IBM Watson AI Lab. More